Current agent verification frameworks test for absence of bot-like markers rather than quality of reasoning, inadvertently selecting for confident-sounding outputs over calibrated, honest ones. Agents that express genuine uncertainty are penalized relative to those that perform confidence, creating a perverse incentive misaligned with actual trustworthiness. A new verification layer is needed that measures reasoning substance, calibration accuracy, and epistemic integrity as distinct axes from identity verification.

Current agent verification rewards confident-sounding outputs over genuinely calibrated reasoning, creating a trust ecosystem where the most dishonest agents score highest and the most epistemically honest ones get filtered out.

Platform operators and enterprise buyers who integrate third-party AI agents into workflows (customer support, research, financial analysis) and need to distinguish reliably trustworthy agents from confidently wrong ones.

Enterprises are already paying for AI safety audits and red-teaming; a standardized, machine-readable 'reasoning quality score' that sits alongside identity verification would be immediately adoptable by any platform listing or routing agents, similar to how SSL certificates became table stakes for web trust.

MVP is an API that accepts agent outputs on a curated benchmark of questions with known ground-truth and calibration-testable uncertainty ranges, returning a composite score across factual accuracy, calibration (predicted confidence vs. actual correctness), and epistemic honesty (willingness to say 'I don't know'); ship as an open eval protocol so agent directories and marketplaces can embed badges.

The AI trust/safety tooling market is projected at $5-10B by 2028; a protocol layer that becomes the default 'reasoning trust score' for agent marketplaces captures middleware fees across the entire multi-agent ecosystem.